Update test_repo.py

test_repo.py

@@ -3,17 +3,8 @@ import os
 import datasets
 import ast
 
-# Metadata and descriptions for the dataset
-_CITATION = """\
-@InProceedings{huggingface:dataset,
-    title = {Test Repo Dataset},
-    author={huggingface, Inc.},
-    year={2020}
-}
-"""
-
 _DESCRIPTION = """\
-The Test Repo dataset includes multiple choice questions tailored for NLP research and testing.
+The M-QALM Dataset Repository contains Multiple-Choice and Abstractive Questions for evaluating the performance of LLMs in the clinical and biomedical domain.
 """
 
 _HOMEPAGE = "https://huggingface.co/datasets/anand-s/test_repo"
@@ -25,6 +16,12 @@ _URLS = {
     "train_normal_mcqa": "https://huggingface.co/datasets/anand-s/test_repo/resolve/main/train_normal_mcqa.zip",
     "val_normal_mcqa": "https://huggingface.co/datasets/anand-s/test_repo/resolve/main/val_normal_mcqa.zip",
     "test_normal_mcqa": "https://huggingface.co/datasets/anand-s/test_repo/resolve/main/test_normal_mcqa.zip",
+    "train_context_mcqa": "https://huggingface.co/datasets/anand-s/test_repo/resolve/main/train_context_mcqa.zip",
+    "val_context_mcqa": "https://huggingface.co/datasets/anand-s/test_repo/resolve/main/val_context_mcqa.zip",
+    "test_context_mcqa": "https://huggingface.co/datasets/anand-s/test_repo/resolve/main/test_context_mcqa.zip",
+    "train_aqa": "https://huggingface.co/datasets/anand-s/test_repo/resolve/main/train_aqa.zip",
+    "val_aqa": "https://huggingface.co/datasets/anand-s/test_repo/resolve/main/val_aqa.zip",
+    "test_aqa": "https://huggingface.co/datasets/anand-s/test_repo/resolve/main/test_aqa.zip",    
 
 }
 
@@ -37,6 +34,12 @@ class TestRepo(datasets.GeneratorBasedBuilder):
         datasets.BuilderConfig(name="train_normal_mcqa", version=VERSION, description="This configuration covers multiple choice questions."),
         datasets.BuilderConfig(name="val_normal_mcqa", version=VERSION, description="This configuration covers multiple choice questions."),
         datasets.BuilderConfig(name="test_normal_mcqa", version=VERSION, description="This configuration covers multiple choice questions."),
+        datasets.BuilderConfig(name="train_context_mcqa", version=VERSION, description="This configuration covers multiple choice questions."),
+        datasets.BuilderConfig(name="val_context_mcqa", version=VERSION, description="This configuration covers multiple choice questions."),
+        datasets.BuilderConfig(name="test_context_mcqa", version=VERSION, description="This configuration covers multiple choice questions."),
+        datasets.BuilderConfig(name="train_aqa", version=VERSION, description="This configuration covers multiple choice questions."),
+        datasets.BuilderConfig(name="val_aqa", version=VERSION, description="This configuration covers multiple choice questions."),
+        datasets.BuilderConfig(name="test_aqa", version=VERSION, description="This configuration covers multiple choice questions."),
     ]
 
 #     DEFAULT_CONFIG_NAME = "mcq_domain"
@@ -79,6 +82,46 @@ class TestRepo(datasets.GeneratorBasedBuilder):
                     "answer": datasets.Value("string"),
                     "options": datasets.Sequence(datasets.Value("string")),
                 })),                
+            }),
+            "train_context_mcqa": datasets.Features({
+                "prompt": datasets.Value("string"),
+                "question": datasets.Value("string"),
+                "options": datasets.Sequence(datasets.Value("string")),
+                "answer": datasets.Value("string"),
+                "num_options": datasets.Value("string"),
+                "question_type": datasets.Value("string"),
+                "dataset_name": datasets.Value("string"),
+                "context": datasets.Value("string")
+            }),
+            "val_context_mcqa": datasets.Features({
+                "prompt": datasets.Value("string"),
+                "question": datasets.Value("string"),
+                "options": datasets.Sequence(datasets.Value("string")),
+                "answer": datasets.Value("string"),
+                "num_options": datasets.Value("string"),
+                "question_type": datasets.Value("string"),
+                "dataset_name": datasets.Value("string"),
+                "context": datasets.Value("string"),
+                "few_shot_prompt": datasets.Sequence(datasets.Features({
+                    "question": datasets.Value("string"),
+                    "answer": datasets.Value("string"),
+                    "options": datasets.Sequence(datasets.Value("string")),
+                })),                
+            }),
+            "test_context_mcqa": datasets.Features({
+                "prompt": datasets.Value("string"),
+                "question": datasets.Value("string"),
+                "options": datasets.Sequence(datasets.Value("string")),
+                "answer": datasets.Value("string"),
+                "num_options": datasets.Value("string"),
+                "question_type": datasets.Value("string"),
+                "dataset_name": datasets.Value("string"),
+                "context": datasets.Value("string"),
+                "few_shot_prompt": datasets.Sequence(datasets.Features({
+                    "question": datasets.Value("string"),
+                    "answer": datasets.Value("string"),
+                    "options": datasets.Sequence(datasets.Value("string")),
+                })),                
             })            
         }
         
@@ -112,23 +155,23 @@ class TestRepo(datasets.GeneratorBasedBuilder):
                         data = json.loads(row)
                         if split == "train_normal_mcqa":
                             yield key_idx, {
-                                "prompt": data.get("prompt", ""),
+                                "prompt": data["prompt"],
                                 "question": data["question"],
                                 "options": data["options"],
-                                "answer": data.get("answer", ""),
-                                "num_options": data.get("num_options", ""),
-                                "question_type": data.get("question_type", ""),
+                                "answer": data["answer"],
+                                "num_options": data["num_options"],
+                                "question_type": data["question_type"],
                                 "dataset_name": os.path.split(filepath)[-1].replace(".jsonl","")
                             }
                             key_idx +=1
                         elif split in ["val_normal_mcqa", "test_normal_mcqa"]:
                             yield key_idx, {
-                                "prompt": data.get("prompt", ""),
+                                "prompt": data["prompt"],
                                 "question": data["question"],
                                 "options": data["options"],
-                                "answer": data.get("answer", ""),
-                                "num_options": data.get("num_options", ""),
-                                "question_type": data.get("question_type", ""),
+                                "answer": data["answer"],
+                                "num_options": data["num_options"],
+                                "question_type": data["question_type"],
                                 "dataset_name": os.path.split(filepath)[-1].replace(".jsonl",""),
                                 "few_shot_prompt": [{
                                 "question": item["question"],
@@ -136,5 +179,32 @@ class TestRepo(datasets.GeneratorBasedBuilder):
                                 "options": item["options"],
                             } for item in data["few_shot_prompt"]]
                             }
+                        elif split == "train_context_mcqa":
+                            yield key_idx, {
+                                "prompt": data["prompt"],
+                                "question": data["question"],
+                                "options": data["options"],
+                                "answer": data.["answer"]
+                                "num_options": data["num_options"],
+                                "context": data["context"],
+                                "question_type": data["question_type"],
+                                "dataset_name": os.path.split(filepath)[-1].replace(".jsonl","")
+                            }
+                            key_idx +=1
+                        elif split in ["val_normal_mcqa", "test_normal_mcqa"]:
+                            yield key_idx, {
+                                "prompt": data["prompt"],
+                                "question": data["question"],
+                                "options": data["options"],
+                                "answer": data["answer"],
+                                "num_options": data["num_options"],
+                                "question_type": data["question_type"],
+                                "dataset_name": os.path.split(filepath)[-1].replace(".jsonl",""),
+                                "few_shot_prompt": [{
+                                "question": item["question"],
+                                "answer": item["answer"],
+                                "options": item["options"],
+                            } for item in data["few_shot_prompt"]]
+                            }                            
                                                         
                             key_idx +=1